OpenRaiser/Intern-Atlas huggingface dataset:
An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations
Venue: arXiv cs.CL 2020
Year: 2020

Document-level discourse parsing, in accordance with the Rhetorical Structure Theory (RST), remains notoriously challenging. Challenges include the deep structure of document-level discourse trees, the requirement of subtle semantic judgments, and the lack of large-scale training corpora. To address such challenges, we propose to exploit robust representations derived from multiple levels of granularity across syntax and semantics, and in turn incorporate such representations in an end-to-end encoder-decoder neural architecture for more resourceful discourse processing. In particular, we first use a pre-trained contextual language model that embodies high-order and long-range dependency to enable finer-grain semantic, syntactic, and organizational representations. We further encode such representations with boundary and hierarchical information to obtain more refined modeling for document-level discourse processing. Experimental results show that our parser achieves the state-of-the-art performance, approaching human-level performance on the benchmarked RST dataset.
Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization
Venue: arXiv cs.CL 2020
Year: 2020

Text summarization is one of the most critical Natural Language Processing (NLP) tasks. More and more researches are conducted in this field every day. Pre-trained transformer-based encoder-decoder models have begun to gain popularity for these tasks. This paper proposes two methods to address this task and introduces a novel dataset named pn-summary for Persian abstractive text summarization. The models employed in this paper are mT5 and an encoder-decoder version of the ParsBERT model (i.e., a monolingual BERT model for Persian). These models are fine-tuned on the pn-summary dataset. The current work is the first of its kind and, by achieving promising results, can serve as a baseline for any future work.
Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead
Venue: arXiv cs.AR 2020
Year: 2020

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance. However, to achieve impressive performance, these algorithms employ very deep networks, requiring a significant computational power, both during the training and inference time. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute- and energy-hungry. In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL execution arises. This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance designs. This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much prominence to the last two solutions since they offer greater design flexibility and bear the potential of high energy-efficiency, especially for the inference process. In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking, explaining how to assess the quality of different networks and hardware systems designed for them.
MDS linear codes with one dimensional hull
Venue: arXiv cs.IT 2020
Year: 2020

We define the Euclidean hull of a linear code $C$ as the intersection of $C$ and its Euclidean dual $C^\perp$. The hull with low dimensions gets much interest due to its crucial role in determining the complexity of algorithms for computing the automorphism group of a linear code and checking permutation equivalence of two linear codes. It has been recently proved that any $q$-ary $[n,k]$ linear code with $q>3$ gives rise to a linear code with the same parameters and having zero dimensional Euclidean hull, which is known as a linear complementary dual code. This paper aims to explore explicit constructions of families of MDS linear codes with one dimensional Euclidean hull. We obtain several classes of such codes.
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
Venue: arXiv cs.LG 2020
Year: 2020

Computer-aided design of molecules has the potential to disrupt the field of drug and material discovery. Machine learning, and deep learning, in particular, have been topics where the field has been developing at a rapid pace. Reinforcement learning is a particularly promising approach since it allows for molecular design without prior knowledge. However, the search space is vast and efficient exploration is desirable when using reinforcement learning agents. In this study, we propose an algorithm to aid efficient exploration. The algorithm is inspired by a concept known in the literature as curiosity. We show on three benchmarks that a curious agent finds better performing molecules. This indicates an exciting new research direction for reinforcement learning agents that can explore the chemical space out of their own motivation. This has the potential to eventually lead to unexpected new molecules that no human has thought about so far.
Centralized Information Interaction for Salient Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

The U-shape structure has shown its advantage in salient object detection for efficiently combining multi-scale features. However, most existing U-shape based methods focused on improving the bottom-up and top-down pathways while ignoring the connections between them. This paper shows that by centralizing these connections, we can achieve the cross-scale information interaction among them, hence obtaining semantically stronger and positionally more precise features. To inspire the potential of the newly proposed strategy, we further design a relative global calibration module that can simultaneously process multi-scale inputs without spatial interpolation. Benefiting from the above strategy and module, our proposed approach can aggregate features more effectively while introducing only a few additional parameters. Our approach can cooperate with various existing U-shape-based salient object detection methods by substituting the connections between the bottom-up and top-down pathways. Experimental results demonstrate that our proposed approach performs favorably against the previous state-of-the-arts on five widely used benchmarks with less computational complexity. The source code will be publicly available.
Optimized Random Forest Model for Botnet Detection Based on DNS Queries
Venue: arXiv cs.CR 2020
Year: 2020

The Domain Name System (DNS) protocol plays a major role in today's Internet as it translates between website names and corresponding IP addresses. However, due to the lack of processes for data integrity and origin authentication, the DNS protocol has several security vulnerabilities. This often leads to a variety of cyber-attacks, including botnet network attacks. One promising solution to detect DNS-based botnet attacks is adopting machine learning (ML) based solutions. To that end, this paper proposes a novel optimized ML-based framework to detect botnets based on their corresponding DNS queries. More specifically, the framework consists of using information gain as a feature selection method and genetic algorithm (GA) as a hyper-parameter optimization model to tune the parameters of a random forest (RF) classifier. The proposed framework is evaluated using a state-of-the-art TI-2016 DNS dataset. Experimental results show that the proposed optimized framework reduced the feature set size by up to 60%. Moreover, it achieved a high detection accuracy, precision, recall, and F-score compared to the default classifier. This highlights the effectiveness and robustness of the proposed framework in detecting botnet attacks.
Collaborative residual learners for automatic icd10 prediction using prescribed medications
Venue: arXiv cs.IR 2020
Year: 2020

Clinical coding is an administrative process that involves the translation of diagnostic data from episodes of care into a standard code format such as ICD10. It has many critical applications such as billing and aetiology research. The automation of clinical coding is very challenging due to data sparsity, low interoperability of digital health systems, complexity of real-life diagnosis coupled with the huge size of ICD10 code space. Related work suffer from low applicability due to reliance on many data sources, inefficient modelling and less generalizable solutions. We propose a novel collaborative residual learning based model to automatically predict ICD10 codes employing only prescriptions data. Extensive experiments were performed on two real-world clinical datasets (outpatient & inpatient) from Maharaj Nakorn Chiang Mai Hospital with real case-mix distributions. We obtain multi-label classification accuracy of 0.71 and 0.57 of average precision, 0.57 and 0.38 of F1-score and 0.73 and 0.44 of accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.
Analysis of Safe Ultrawideband Human-Robot Communication in Automated Collaborative Warehouse
Venue: arXiv cs.RO 2020
Year: 2020

The paper presents the propagation analysis of ultrawideband Gaussian signal in an automated collaborative warehouse environment where human and robots communicate to ensure that mutual collisions do not occur. The warehouse racks are principally modeled as clusters of metallic (PEC) parallelepipeds, with dimensions chosen to approximate the realistic warehouse. The signal propagation is analyzed using a ray tracing software, with the goal to calculate the path loss profile for different representative scenarios and antenna polarizations. The influence of the rack surface roughness onto propagation is also analyzed. The guidelines for optimum antenna positions on humans and robots for safe communication are proposed according to the simulations results.
Self-attention Comparison Module for Boosting Performance on Retrieval-based Open-Domain Dialog Systems
Venue: arXiv cs.CL 2020
Year: 2020

Since the pre-trained language models are widely used, retrieval-based open-domain dialog systems, have attracted considerable attention from researchers recently. Most of the previous works select a suitable response only according to the matching degree between the query and each individual candidate response. Although good performance has been achieved, these recent works ignore the comparison among the candidate responses, which could provide rich information for selecting the most appropriate response. Intuitively, better decisions could be made when the models can get access to the comparison information among all the candidate responses. In order to leverage the comparison information among the candidate responses, in this paper, we propose a novel and plug-in Self-attention Comparison Module for retrieval-based open-domain dialog systems, called SCM. Extensive experiment results demonstrate that our proposed self-attention comparison module effectively boosts the performance of the existing retrieval-based open-domain dialog systems. Besides, we have publicly released our source codes for future research.
Ising-Based Louvain Method: Clustering Large Graphs with Specialized Hardware
Venue: arXiv cs.SI 2020
Year: 2020

Recent advances in specialized hardware for solving optimization problems such quantum computers, quantum annealers, and CMOS annealers give rise to new ways for solving real-word complex problems. However, given current and near-term hardware limitations, the number of variables required to express a large real-world problem easily exceeds the hardware capabilities, thus hybrid methods are usually developed in order to utilize the hardware. In this work, we advocate for the development of hybrid methods that are built on top of the frameworks of existing state-of-art heuristics, thereby improving these methods. We demonstrate this by building on the so called Louvain method, which is one of the most popular algorithms for the Community detection problem and develop and Ising-based Louvain method. The proposed method outperforms two state-of-the-art community detection algorithms in clustering several small to large-scale graphs. The results show promise in adapting the same optimization approach to other unsupervised learning heuristics to improve their performance.
Identifying opinion-based groups from survey data: a bipartite network approach
Venue: arXiv cs.SI 2020
Year: 2020

A survey can be represented by a bipartite network as it has two types of nodes, participants and items in which participants can only interact with items. We introduce an agreement threshold to take a minimal projection of the participants linked by shared responses in order to identify opinion-based groups. We show that in American National Election Studies-data, this can identify polarisation along political attitudes. We also take a projection of attitudes that are linked by how participants respond to them. This can be used to show which attitudes are commonly held together in different countries or communities.
Comparing Suicide Risk Insights derived from Clinical and Social Media data
Venue: arXiv cs.SI 2020
Year: 2020

Suicide is the 10th leading cause of death in the US and the 2nd leading cause of death among teenagers. Clinical and psychosocial factors contribute to suicide risk (SRFs), although documentation and self-expression of such factors in EHRs and social networks vary. This study investigates the degree of variance across EHRs and social networks. We performed subjective analysis of SRFs, such as self-harm, bullying, impulsivity, family violence/discord, using >13.8 Million clinical notes on 123,703 patients with mental health conditions. We clustered clinical notes using semantic embeddings under a set of SRFs. Likewise, we clustered 2180 suicidal users on r/SuicideWatch (~30,000 posts) and performed comparative analysis. Top-3 SRFs documented in EHRs were depressive feelings (24.3%), psychological disorders (21.1%), drug abuse (18.2%). In r/SuicideWatch, gun-ownership (17.3%), self-harm (14.6%), bullying (13.2%) were Top-3 SRFs. Mentions of Family violence, racial discrimination, and other important SRFs contributing to suicide risk were missing from both platforms.
The COVID-19 pandemic: socioeconomic and health disparities
Venue: arXiv cs.LG 2020
Year: 2020

Disadvantaged groups around the world have suffered and endured higher mortality during the current COVID-19 pandemic. This contrast disparity suggests that socioeconomic and health-related factors may drive inequality in disease outcome. To identify these factors correlated with COVID-19 outcome, country aggregate data provided by the Lancet COVID-19 Commission subjected to correlation analysis. Socioeconomic and health-related variables were used to predict mortality in the top 5 most affected countries using ridge regression and extreme gradient boosting (XGBoost) models. Our data reveal that predictors related to demographics and social disadvantage correlate with COVID-19 mortality per million and that XGBoost performed better than ridge regression. Taken together, our findings suggest that the health consequence of the current pandemic is not just confined to indiscriminate impact of a viral infection but that these preventable effects are amplified based on pre-existing health and socioeconomic inequalities.
Universal Policies for Software-Defined MDPs
Venue: arXiv cs.AI 2020
Year: 2020

We introduce a new programming paradigm called oracle-guided decision programming in which a program specifies a Markov Decision Process (MDP) and the language provides a universal policy. We prototype a new programming language, Dodona, that manifests this paradigm using a primitive 'choose' representing nondeterministic choice. The Dodona interpreter returns either a value or a choicepoint that includes a lossless encoding of all information necessary in principle to make an optimal decision. Meta-interpreters query Dodona's (neural) oracle on these choicepoints to get policy and value estimates, which they can use to perform heuristic search on the underlying MDP. We demonstrate Dodona's potential for zero-shot heuristic guidance by meta-learning over hundreds of synthetic tasks that simulate basic operations over lists, trees, Church datastructures, polynomials, first-order terms and higher-order terms.
Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

Research in machine learning is at a turning point. While supervised deep learning has conquered the field at a breathtaking pace and demonstrated the ability to solve inference problems with unprecedented accuracy, it still does not quite live up to its name if we think of learning as the process of acquiring knowledge about a subject or problem. Major weaknesses of present-day deep learning models are, for instance, their lack of adaptability to changes of environment or their incapability to perform other kinds of tasks than the one they were trained for. While it is still unclear how to overcome these limitations, one can observe a paradigm shift within the machine learning community, with research interests shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks, and towards employing machine learning algorithms in highly diverse domains. This research question can be approached from different angles. For instance, the field of Informed AI investigates the problem of infusing domain knowledge into a machine learning model, by using techniques such as regularization, data augmentation or post-processing. On the other hand, a remarkable number of works in the recent years has focused on developing models that by themselves guarantee a certain degree of versatility and invariance with respect to the domain or problem at hand. Thus, rather than investigating how to provide domain-specific knowledge to machine learning models, these works explore methods that equip the models with the capability of acquiring the knowledge by themselves. This white paper provides an introduction and discussion of this emerging field in machine learning research. To this end, it reviews the role of knowledge in machine learning, and discusses its relation to the concept of invariance, before providing a literature review of the field.
TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network
Venue: arXiv cs.CL 2020
Year: 2020

This paper illustrates the details description of technical text classification system and its results that developed as a part of participation in the shared task TechDofication 2020. The shared task consists of two sub-tasks: (i) first task identify the coarse-grained technical domain of given text in a specified language and (ii) the second task classify a text of computer science domain into fine-grained sub-domains. A classification system (called 'TechTexC') is developed to perform the classification task using three techniques: convolution neural network (CNN), bidirectional long short term memory (BiLSTM) network, and combined CNN with BiLSTM. Results show that CNN with BiLSTM model outperforms the other techniques concerning task-1 of sub-tasks (a, b, c and g) and task-2a. This combined model obtained f1 scores of 82.63 (sub-task a), 81.95 (sub-task b), 82.39 (sub-task c), 84.37 (sub-task g), and 67.44 (task-2a) on the development dataset. Moreover, in the case of test set, the combined CNN with BiLSTM approach achieved that higher accuracy for the subtasks 1a (70.76%), 1b (79.97%), 1c (65.45%), 1g (49.23%) and 2a (70.14%).
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective
Venue: arXiv cs.LG 2020
Year: 2020

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This missingness, if ignored, nullifies any fairness guarantee of the training procedure when the model is deployed. Using causal graphs, we characterize the missingness mechanisms in different real-world scenarios. We show conditions under which various distributions, used in popular fairness algorithms, can or can not be recovered from the training data. Our theoretical results imply that many of these algorithms can not guarantee fairness in practice. Modeling missingness also helps to identify correct design principles for fair algorithms. For example, in multi-stage settings where decisions are made in multiple screening rounds, we use our framework to derive the minimal distributions required to design a fair algorithm. Our proposed algorithm decentralizes the decision-making process and still achieves similar performance to the optimal algorithm that requires centralization and non-recoverable distributions.
New Recommendation Algorithm for Implicit Data Motivated by the Multivariate Normal Distribution
Venue: arXiv cs.IR 2020
Year: 2020

The goal of recommender systems is to help users find useful items from a large catalog of items by producing a list of item recommendations for every user. Data sets based on implicit data collection have a number of special characteristics. The user and item interaction matrix is often complete, i.e. every user and item pair has an interaction value or zero for no interaction, and the goal is to rank the items for every user. This study presents a simple new algorithm for implicit data that matches or outperforms baselines in accuracy. The algorithm can be motivated intuitively by the Multivariate Normal Distribution (MVN), where have a closed form expression for the ranking of non-interactions given user's interactions. The main difference to kNN and SVD baselines is that predictions are carried out using only the known interactions. Modified baselines with this trick have a better accuracy, however it also results in simpler models with fewer hyperparameters for implicit data. Our results suggest that this idea should used in Top-N recommendation with small seed sizes and the MVN is a simple way to do so.